Involvement and targeted intervention of dysregulated Hedgehog signaling in osteosarcoma
Bibliographic record
Abstract
BACKGROUND: During development, the Hedgehog pathway plays important roles regulating the proliferation and differentiation of chondrocytes, providing a template for growing bone. In this study, the authors investigated the components of dysregulated Hedgehog signaling as potential therapeutic targets for osteosarcoma. METHODS: Small-molecule agonists and antagonists that modulate the Hedgehog pathway at different levels were used to investigate the mechanisms of dysregulation and the efficacy of Hedgehog blockade in osteosarcoma cell lines. The inhibitory effect of a small-molecule Smoothened (SMO) antagonist, IPI-926 (saridegib), also was examined in patient-derived xenograft models. RESULTS: An inverse correlation was identified in osteosarcoma cell lines between endogenous glioma-associated oncogene 2 (GLI2) levels and Hedgehog pathway induction levels. Cells with high levels of GLI2 were sensitive to GLI inhibition, but not SMO inhibition, suggesting that GLI2 overexpression may be a mechanism of ligand-independent activation. In contrast, cells that expressed high levels of the Hedgehog ligand gene Indian hedgehog (IHH) and the target genes patched 1 (PTCH1) and GLI1 were sensitive to modulation of both SMO and GLI, suggesting ligand-dependent activation. In 2 xenograft models, active autocrine and paracrine, ligand-dependent Hedgehog signaling was identified. IPI-926 inhibited the Hedgehog signaling interactions between the tumor and the stroma and demonstrated antitumor efficacy in 1 of 2 ligand-dependent models. CONCLUSIONS: The current results indicate that both ligand-dependent and ligand-independent Hedgehog dysregulation may be involved in osteosarcoma. It is the first report to demonstrate Hedgehog signaling crosstalk between the tumor and the stroma in osteosarcoma. The inhibitory effect of IPI-926 warrants additional research and raises the possibility of using Hedgehog pathway inhibitors as targeted therapeutics to improve treatment for osteosarcoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".